Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.
https://anaconda.org/imperial-college-research-computing/r-hypervolume/badges/version.svg
https://anaconda.org/imperial-college-research-computing/r-hypervolume/badges/latest_release_date.svg
https://anaconda.org/imperial-college-research-computing/r-hypervolume/badges/latest_release_relative_date.svg
https://anaconda.org/imperial-college-research-computing/r-hypervolume/badges/platforms.svg
https://anaconda.org/imperial-college-research-computing/r-hypervolume/badges/license.svg
https://anaconda.org/imperial-college-research-computing/r-hypervolume/badges/downloads.svg